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首页> 外文期刊>Nordic hydrology >Improving ANN model performance in runoff forecasting by adding soil moisture input and using data preprocessing techniques
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Improving ANN model performance in runoff forecasting by adding soil moisture input and using data preprocessing techniques

机译：通过增加土壤水分输入并使用数据预处理技术来改善径流预测中的ANN模型性能

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摘要

This study attempts to improve the accuracy of runoff forecasting from two aspects: one is the inclusion of soil moisture time series simulated from the GR4J conceptual rainfall–runoff model as (ANN) input; the other is preprocessing original data series by singular spectrum analysis (SSA). Three watersheds in China were selected as case studies and the ANN1 model only with runoff and rainfall as inputs without data preprocessing was used to be the benchmark. The ANN2 model with soil moisture as an additional input, the SSA-ANN1 and SSA-ANN2 models with the same inputs as ANN1 and ANN2 using data preprocessing were studied. It is revealed that the degree of improvement by SSA is more significant than by the inclusion of soil moisture. Among the four studied models, the SSA-ANN2 model performs the best.
机译：这项研究试图从两个方面提高径流预报的准确性：一个是将GR4J概念性降雨-径流模型作为（ANN）输入来模拟土壤水分时间序列。另一种是通过奇异频谱分析（SSA）预处理原始数据系列。选择了中国的三个流域作为案例研究，以仅以径流和降雨作为输入而没有数据预处理的ANN1模型作为基准。利用数据预处理研究了以土壤水分为附加输入的ANN2模型，与ANN1和ANN2具有相同输入的SSA-ANN1和SSA-ANN2模型。结果表明，SSA的改良程度比土壤水分的改良程度更重要。在所研究的四个模型中，SSA-ANN2模型表现最佳。

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